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NEW QUESTION: 1
What are valid options for Network Access Device Settings? (Select two.)
A. You can configure SNMP Read Settings to monitor the load of a NAD in order not to overload it with the requests.
B. The OnConnect Enforcement allows you to enable specific ports that trigger Enforcement when any device connects.
C. You can configure SNMP Write Settings to send commands to the devices that do not support other methods.
D. In CLI settings, you can define the access credentials and the command templates that will be used.
E. On the Attributes tab. you can enable the service to write attributes like Location and Device type based on policy.
Answer: B,E
NEW QUESTION: 2
A. Option B
B. Option C
C. Option A
D. Option D
Answer: C,D
Explanation:
A: Elements in the deployment descriptor
The description of a MDB (message-driven beans) in the EJB 2.0 deployment descriptor contains the following specific elements:
*the JMS acknowledgement mode: auto-acknowledge or dups-ok-acknowledge
*an eventual JMS message selector: this is a JMS concept which allows the filtering of the
messages sent to the destination
*a message-driven-destination, which contains the destination type (Queue or Topic) and the
subscription
D: Example:
The following example is a basic message-driven bean:
@MessageDriven(activationConfig={
@ActivationConfigProperty(propertyName="destination", propertyValue="myDestination"),
@ActivationConfigProperty(propertyName="destinationType", propertyValue="javax.jms.Queue")
})
public class MsgBean implements javax.jms.MessageListener {
public void onMessage(javax.jms.Message msg) {
String receivedMsg = ((TextMessage) msg).getText();
System.out.println("Received message: " + receivedMsg);
}
}
Reference: Developing message-driven beans
Reference: Message Driven Beans Tutorial
NEW QUESTION: 3
Azure Machine Learning Studioを使用して実験を作成しています。
評価のためにデータを4つのサブセットに分割する必要があります。データには高度な欠損値があります。
分析用のデータを準備する必要があります。
実験を作成するための適切な方法を選択する必要があります。
どの3つのモジュールを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
注：回答の選択肢の複数の順序が正しいです。選択した正しい注文のいずれかのクレジットを受け取ります。
Answer:
Explanation:
Explanation:
The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.
Incorrect Answers:
Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.
Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.
Missing Value Scrubber: The Missing Values Scrubber module is deprecated.
Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.
Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
NEW QUESTION: 4
A company implements Dynamics 365 for Customer Service.
Which status reason is used for each casestatus? To answer, select the appropriate option in the answer area.
NOTE:Each correct selection is worth one point.
Answer:
Explanation:
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